Pengaruh Penerapan Model Pembelajaran Quantum Learning Terhadap Hasil Belajar Siswa pada Mata Pelajaran Informatika di MTsN 6 Agam

This research is motivated by several problems that the authors found in class VII MTsN 6 Agam, especially in the Informatics subject. Where in learning there are still some student scores that are under the Minimum Completeness Criteria (KKM) that have been determined. Various problems arise from students who are less interested, tend to be bored, and lack interest and motivation of students in participating in learning. The type of research used in this research is quantitative research, namely Quasy experimental design. The population in this study were class VII students of MTsN 6 Religion for the 2022/2023 Academic Year. Sampling was carried out by purposive sampling technique, namely sampling with certain considerations. The population of class VII is 157 students. Based on the results of the daily (initial) test, the average class VII 7 was used as the Experimental class, while students from class VII 5 were used as the control class. The research data was obtained by the final test in the form of multiple questions of 20 items that have been tested. The results of the study show that student learning outcomes in Informatics subjects using the Quantum Learning learning model are included in the good category, there is a significant influence in applying the Quantum Learning learning model, the learning outcomes of students using the Quantum Learning learning model are higher compared to students using Quantum Learning learning. conventional.Based on the results of the final test of the two sample classes, the experimental class had an average score of 90.00 compared to the control class, which was 77.03. The posttest hypothesis test with the t-test shows that at level α = 0.05 is tcount = 144.91 > ttable = 0.358, so it can be concluded that the effect of applying the Quantum Learning learning model affects student learning outcomes in class VII informatics subject MTsN 6 Agam. Then H1 is accepted, meaning that there is a significant difference between the two learning models, namely the Quantum Learning model and the Conventional learning model. Thus H0 is rejected.